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Hidden vector state model for hierarchical semantic parsing

He, Yulan and Young, S. (2003). Hidden vector state model for hierarchical semantic parsing. Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference, 1 pp. 268–271.

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The paper presents a hidden vector state (HVS) model for hierarchical semantic parsing. The model associates each state of push-down automata with the state of an HMM. State transitions are factored into separate stack pop and push operations and then constrained to give a tractable search space. The result is a model which is complex enough to capture hierarchical structure but which can be trained automatically from unannotated data. Experiments have been conducted on ATIS-3 1993 and 1994 test sets. The results show that the HVS model outperforms a general finite state tagger (FST) by 19% to 32% in error reduction.

Item Type: Journal Article
Copyright Holders: 2003 IEEE
ISSN: 1520-6149
Keywords: HMM; error reduction; general finite state tagger; hidden vector state model; hierarchical semantic parsing; push-down automata; search space; speech recognition; spoken dialogue systems; stack pop operations; stack push operations
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 23778
Depositing User: Kay Dave
Date Deposited: 29 Mar 2011 10:31
Last Modified: 25 Mar 2016 21:28
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